SQEIR: An epidemic virus spread analysis and prediction model. (September 2022)

• Record Type: Journal Article
• Authors: Wu, Yichun; Sun, Yaqi; Lin, Mugang
• Is Part Of: Computers & electrical engineering. Volume 102(2022)
• Publication Date: 2022-09
• Subjects: Susceptible quarantined exposed infective -- Infectious disease model -- Mathematical model -- COVID-19; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2022.108230
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: New infectious disease SQEIR model proposed to predict virus spread. Optimal estimation of the number of infected individuals by using the least squares method. Formulation of g a new differential equation to describe the population distribution. Abstract: In 2019, a new strain of coronavirus pneumonia spread quickly worldwide. Viral propagation may be simulated using the Susceptible Infectious Removed (SIR) model. However, the SIR model fails to consider that separation of patients in the COVID-19 incubation stage entails difficulty and that these patients have high transmission potential. The model also ignores the positive effect of quarantine measures on the spread of the epidemic. To address the two flaws in the SIR model, this study proposes a new infectious disease model referred to as the Susceptible Quarantined Exposed Infective Removed (SQEIR) model. The proposed model uses the weighted least squares for the optimal estimation of important parameters in the infectious disease model. Based on these parameters, new differential equations were developed to describe the spread of the epidemic. The experimental results show that this model exhibits an accuracy 6.7% higher than that of traditional infectious disease models. Graphical Abstract: Image, graphical abstract